Detection of pan-Arctic terrestrial snowmelt from QuikSCAT, 2000–2005

Wang, Libo; Derksen, Chris; Brown, Ross

doi:10.1016/j.rse.2008.05.017

Cited by 63 publications

(111 citation statements)

References 34 publications

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“…The three of five day algorithm has proven accurate based on manual cross checking of observations and correspondence with estimates from earlier work, and allows the melt onset and melt refreeze end to be automatically detected for large regions such as the YRB. A similar approach was previously utilized with QuikSCAT (Quick Scatterometer) where melt onset was identified when the difference was greater than a threshold for three or more consecutive days and the intensity calculated as the accumulated decrease in radar cross section in relation to the five day mean (Wang et al, 2008). A similar threshold based passive microwave melt detection approach was previously applied successfully over a wide spatial domain in the pan-Arctic study by Tedesco et al (2009).…”

Section: Methodsmentioning

confidence: 99%

“…Results from the QuikSCAT melt detection revealed that melt onset occurred in the middle to the end of March for boreal forest areas, increased with latitude, and was later over high elevation areas and for years with a cold spring season. Melt end dates were later over lake-rich areas and had more interannual variability than onset dates, while melt duration was longer for areas with deeper snow cover (Wang et al, 2008). An integrated pan-Arctic melt onset date dataset from active and passive microwave satellites further elucidated melt progression over various land types and determined that elevation, tree fraction and latitude largely explained mean melt onset date in the terrestrial Arctic .…”

Section: K a Semmens And J M Ramage: Recent Changes In Spring Snomentioning

confidence: 99%

“…The melt onset and melt end dates were found to have significant negative trends with melt starting 0.5 d yr −1 earlier and ending 1 d yr −1 earlier over the past 30 yr, thus showing a shortened melt season of 0.6 d yr −1 (Tedesco et al, 2009). Melt onset and snow-off dates for the pan-Arctic were also detected from enhanced resolution SeaWinds scatterometer QuikSCAT data from 2000-2005 using a melt algorithm that identifies multiple melt events, their duration and intensity, and comparing the differences between daily time series radar data and the previous five day average (Wang et al, 2008). Results from the QuikSCAT melt detection revealed that melt onset occurred in the middle to the end of March for boreal forest areas, increased with latitude, and was later over high elevation areas and for years with a cold spring season.…”

Section: K a Semmens And J M Ramage: Recent Changes In Spring Snomentioning

confidence: 99%

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Recent changes in spring snowmelt timing in the Yukon River basin detected by passive microwave satellite data

Semmens

Ramage

2013

The Cryosphere

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Abstract. Spring melt is a significant feature of high latitude snowmelt dominated drainage basins influencing hydrological and ecological processes such as snowmelt runoff and green-up. Melt duration, defined as the transition period from snowmelt onset until the end of the melt refreeze, is characterized by high diurnal amplitude variations (DAV) where the snowpack is melting during the day and refreezing at night, after which the snowpack melts constantly until depletion. Determining trends for this critical period is necessary for understanding how the Arctic is changing with rising temperatures and provides a baseline from which to assess future change. To study this dynamic period, brightness temperature (T b ) data from the Special Sensor Microwave Imager (SSM/I) 37 V-GHz frequency from 1988 to 2010 were used to assess snowmelt timing trends for the Yukon River basin, Alaska/Canada. Annual T b and DAV for 1434 Equal-Area Scalable Earth (EASE)-Grid pixels (25 km resolution) were processed to determine melt onset and melt refreeze dates from T b and DAV thresholds previously established in the region. Temporal and spatial trends in the timing of melt onset and melt refreeze, and the duration of melt were analyzed for the 13 sub-basins of the Yukon River basin with three different time interval approaches. Results show a lengthening of the melt period for the majority of the sub-basins with a significant trend toward later end of melt refreeze after which the snowpack melts day and night leading to snow clearance, peak discharge, and green-up. Earlier melt onset trends were also found in the higher elevations and northernmost subbasins (Porcupine, Chandalar, and Koyukuk rivers). Latitude and elevation displayed the dominant controls on melt timing variability and spring solar flux was highly correlated with melt timing in middle (∼ 600-1600 m) elevations.

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Section: Methodsmentioning

confidence: 99%

Section: K a Semmens And J M Ramage: Recent Changes In Spring Snomentioning

confidence: 99%

Section: K a Semmens And J M Ramage: Recent Changes In Spring Snomentioning

confidence: 99%

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Recent changes in spring snowmelt timing in the Yukon River basin detected by passive microwave satellite data

Semmens

Ramage

2013

The Cryosphere

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“…It is possible to map snow cover using Synthetic Aperture Radar (SAR) data, but several limitations prevent their consideration for the presented study: Snow cover only becomes visible under wet snow conditions [21,26], and the temporal resolution of SAR sensors is typically too low to derive daily snow cover information for an area as big as the study region of Central Asia (11, 24, 35 days for TerraSAR-X, RADARSAT, Envisat/ASAR data, respectively). Additionally, the time series of available SAR data only ranges back until 1995 (RADARSAT).…”

Section: Data Sourcesmentioning

confidence: 99%

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

et al. 2014

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Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km 2 . The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.

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“…Previous studies of the F/T state tended to focus on particular areas [8][9][10] or applications, such as reindeer husbandry [11], vegetation phenology [12,13] or hydrology [14]. There is also interest in global maps, which have been derived from passive [15], as well as active data, e.g., the Surface State Flag (SSF) [16], based on thresholding of ASCAT backscatter measurements.…”

Section: Introductionmentioning

confidence: 99%

Frozen Soil Detection Based on Advanced Scatterometer Observations and Air Temperature Data as Part of Soil Moisture Retrieval

2015

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Surface soil moisture is one of the operational products derived from Advanced Scatterometer (ASCAT) data. The reliability of its estimation depends on the detection of predominantly frozen conditions of the landscape (including soil and vegetation) and the presence of wet snow, which would otherwise impede the estimation. As the robust determination of the freeze/thaw (F/T) state using exclusively scatterometer measurements on a global basis is complicated due to the myriad of different climatic and land cover conditions; we propose to support the retrieval using ERA Interim temperature data. The approach is based on a probabilistic time series model, whereby backscatter and temperature data are combined to estimate the freeze/thaw state. The method is assessed with proxy F/T states derived from modeled and in situ air and soil temperature data on a global basis. These analyses show an improved consistency compared to a previously published ASCAT F/T algorithm, with typical agreements between the external data and the results of the algorithm exceeding 80%. The quantitative interpretation of these comparisons is, however, hampered by discrepancies between the F/T state derived from temperature data and the one pertinent to radar remote sensing, as the former does not account for, e.g., wet snow conditions. The inclusion of the ERA Interim temperature data can improve the accuracy of the algorithm by more than 10 percentage points in regions where freezing conditions are rare.Remote Sens. 2015, 7 3207

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Detection of pan-Arctic terrestrial snowmelt from QuikSCAT, 2000–2005

Cited by 63 publications

References 34 publications

Recent changes in spring snowmelt timing in the Yukon River basin detected by passive microwave satellite data

Recent changes in spring snowmelt timing in the Yukon River basin detected by passive microwave satellite data

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Frozen Soil Detection Based on Advanced Scatterometer Observations and Air Temperature Data as Part of Soil Moisture Retrieval

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